Linguistic Factors in Statistical Machine Translation Involving Arabic Language

Arabic is considered to have a rich morphology compared to English language. This fact adversely affects the performance of English-Arabic Statistical Machine Translation (SMT). Phrasebased SMT models have a limitation of mapping phrases or blocks from the source to the target languages without any use of linguistic information. Incorporating linguistic tools, such as part-of-speech (POS) taggers can have an impact on translation quality. In this paper, the use of POS tagging is incorporated as a linguistic feature in a factored translation model. The use of factored translation model and its impact on translation quality for English-Arabic machine translation is reported.